This invention relates to plasma generation equipment, and is particularly directed to an automatic RF matching network to match the impedance of a reactive plasma chamber or similar non-linear load to a constant impedance (e.g., 50 ohms) output of an RF generator or similar RF source. The invention is more particularly concerned with a fuzzy logic technique that is capable of controlling two, or more, tunable elements in the matching network using both the phase error signal and magnitude error signal associated with the matching network.
In a typical RF plasma generator arrangement, a high power RF source produces an RF wave at a preset frequency, i.e., 13.56 MHZ, and this is furnished along a power conduit to a plasma chamber. The RF power is also typically provided at a fixed, known impedance, e.g., 50 ohms. Because there is typically a severe impedance mismatch between the RF power source and the plasma chamber, an impedance matching network is interposed between the two. There are non-linearities in the plasma chamber which make it difficult to simply set the impedance match network at fixed positions for a plasma process. At the input to the matching network there is located a phase and magnitude error detector that produces two (or more) error signals representing the magnitude of impedance error and phase error. Magnitude error is the difference between the magnitude of the nominal input impedance (typically 50Ω and the magnitude of the actual input impedance. Phase error is the deviation between the phase at the nominal input impedance (typically zero degrees) and the phase at the actual input impedance. The error signals also indicate the direction or sign (+ or −) of the magnitude error and phase error.
The conventional matching network uses these two error signals to control two variable tuning elements: phase error being used to control one tuning element and magnitude error being used to control the other tuning element. The phase and magnitude error signals drive motors associated with variable capacitors or perhaps a tuning slug of a variable inductor. The error signals drop to a low or zero level when a matched condition has been achieved.
The conventional system has experienced difficulty in quickly achieving matched impedance under a number of conditions. One primary problem is that the current design does not address the fact that each tuning element affects both error signals. Because of this effect, the error signals may drive one or both of the tuning elements away from the match or tune point. This prolongs the tuning process, and causes slower, less reliable tuning. Another problem arises because the phase and magnitude error signals alone do not always provide enough information to drive the matching network to the tune point. This means that the matching network may have “lost conditions” where it will be unable to reach impedance match. A third problem is that the error signal produced by a given movement of a tuning element varies with the tuning element's position. In other words, if the tuning element is near the minimum end of its travel, a 10% change in position may produce a 50% change in error signal amplitude, but if the tuning element is near the maximum end of its travel, the same 10% change might produce only a 5% change in error signal amplitude. This will cause the control loop stability to vary depending upon the position of the tuning elements. However, at the present time, no practical system even tracks the tuning element (e.g., rotor) position as an input.
A cross-point approach to address the first-mentioned problem has been proposed previously, but still uses only a single error signal for each of two tuning elements. Another problem is that this approach requires a hard, fixed threshold rather than a gradual transition.
A lost-recovery approach has been proposed to address the second problem mentioned above, namely the “lost conditions” problem. In this approach the system detects that impedance match has been lost, and then moves the tuning elements to predetermined “lost recovery” positions, from which it can tune to a match. This approach wastes considerable time in recovering impedance match, and may not work with every load in the tuning range.
The industry does not seem to have recognized the third problem arising from the non-linearity of the error signal across its range. Also, the desirability of using more than one error signal to control each tuning element has not been recognized, nor has any process been proposed for combining multiple error signals to control each of the tuning elements associated with the impedance matching network.
Fuzzy logic has been employed as a control algorithm in many applications, and has the advantage of reducing a complex multi-dimensional treatment to a rather straight-forward algorithm based on a simple set of rules. Fuzzy logic is based on Fuzzy Set Theory, a branch of mathematics developed by Prof. Lofti A. Zadeh in the 1960's. Fuzzy logic provides a robust, non-linear and efficient approach to control by mapping inputs to outputs using a minimal amount of code. The basis for fuzzy logic and fuzzy controls has been explained in many places in the mathematical and engineering literature. Basically, the fuzzy logic control process can be described as a small number steps or stages. First, the process control engineer establishes a number of overlapping fuzzy sets, e.g., “high positive,” “medium positive,” “zero” or small, “medium negative,” and “high negative.” In the first stage, i.e., “fuzzification,” crisp, discrete input values are fuzzified, that is, they are converted into appropriate degrees of membership in overlapping fuzzy sets. Then, in a rule application stage, rules are applied to define the relation between the input variables and the output variables. These rules are provided in terms of a “fuzzy interference function” and represent a relation that should be intuitive to the process engineer. These can be a series of IF-AND-THEN statements, or can be constructed as a straightforward table, grid or matrix. An output or defuzzification stage converts the fuzzy variables to crisp output values that are applied as control values or signals to a control device, such as the rotor of a variable capacitor.
No one has previously considered employing fuzzy logic to the control the tuning of an impedance match network, and no one has previously appreciated that an application of fuzzy logic would resolve the three problems mentioned above.